Mood, Emotive Content, and Reasoning

Mood, Emotive Content, and Reasoning - Daniel Zahra Theories of how individuals reason, and how they experience emotion abound in the psychological literature; yet, despite the common lay-theories of how emotions might affect a person’s reasoning, very little empirical work has been conducted on this relationship. The current thesis addresses this knowledge-gap by first distilling from the literature two classes of emotion theory; Information, and Load; and then systematically testing the explanatory power of these theories. A dual-process framework is employed in order to define low (Type One) and high effort (Type Two) strategies. Information theories predict that negative emotion cues more analytic processing relative to positive emotion, whereas load theories predict both positive and negative emotion to suppress use of high-effort strategies. Thus the two theories are compared by varying incidental and integral emotion across syllogistic reasoning, conditional reasoning, and the ratio-bias task, and assessing the engagement of Type One and Type Two processes across positive emotion, negative emotion, and control conditions. The findings suggest that emotion effects in syllogistic reasoning do not consistently support either Load or Information theories (Experiments 1-4). Emotion effects are found to be typically larger for integral than incidental emotion (Experiment 5), and most frequently serve as Information in verbal (Experiments 6 and 7) and visual conditional reasoning tasks (Experiment 8). Furthermore, these effects are to a large extent dependent on task properties such as the number of alternative antecedents (Experiments 9 and 10), and are greater on more difficult tasks (Experiments 11 and 12). These findings suggest that emotion has a greater impact on Type Two than Type One processes. A range of methodological and theoretical implications which will inform future work in this area are also discussed in the closing chapter.ESR

Assessment of knee flexion in young children with prosthetic knee components using dynamic time warping

Introduction: Analysis of human locomotion is challenged by limitations in traditional numerical and statistical methods as applied to continuous timeseries data. This challenge particularly affects understanding of how close limb prostheses are to mimicking anatomical motion. This study was the first to apply a technique called Dynamic Time Warping to measure the biomimesis of prosthetic knee motion in young children and addressed the following research questions: Is a combined dynamic time warping/root mean square analysis feasible for analyzing pediatric lower limb kinematics? When provided at an earlier age than traditional protocols dictate, can children with limb loss utilize an articulating prosthetic knee in a biomimetic manner? Methods: Warp costs and amplitude differences were generated for knee flexion curves in a sample of ten children five years of age and younger: five with unilateral limb loss and five age-matched typically developing children. Separate comparisons were made for stance phase flexion and swing phase flexion via two-way ANOVAs between bilateral limbs in both groups, and between prosthetic knee vs. dominant anatomical knee in age-matched pairs between groups. Greater warp costs indicated greater temporal dissimilarities, and a follow-up root mean square assessed remaining amplitude dissimilarities. Bilateral results were assessed by age using linear regression. Results: The technique was successfully applied in this population. Young children with limb loss used a prosthetic knee biomimetically in both stance and swing, with mean warp costs of 12.7 and 3.3, respectively. In the typically developing group, knee motion became more symmetrical with age, but there was no correlation in the limb loss group. In all comparisons, warp costs were significantly greater for stance phase than swing phase. Analyses were limited by the small sample size. Discussion: This study has established that dynamic time warping with root mean square analysis can be used to compare the entirety of time-series curves generated in gait analysis. The study also provided clinically relevant insights on the development of mature knee flexion patterns during typical development, and the role of a pediatric prosthetic knee

On the difficulty of learning chaotic dynamics with RNNs

Recurrent neural networks (RNNs) are wide-spread machine learning tools for modeling sequential and time series data. They are notoriously hard to train because their loss gradients backpropagated in time tend to saturate or diverge during training. This is known as the exploding and vanishing gradient problem. Previous solutions to this issue either built on rather complicated, purpose-engineered architectures with gated memory buffers, or - more recently - imposed constraints that ensure convergence to a fixed point or restrict (the eigenspectrum of) the recurrence matrix. Such constraints, however, convey severe limitations on the expressivity of the RNN. Essential intrinsic dynamics such as multistability or chaos are disabled. This is inherently at disaccord with the chaotic nature of many, if not most, time series encountered in nature and society. It is particularly problematic in scientific applications where one aims to reconstruct the underlying dynamical system. Here we offer a comprehensive theoretical treatment of this problem by relating the loss gradients during RNN training to the Lyapunov spectrum of RNN-generated orbits. We mathematically prove that RNNs producing stable equilibrium or cyclic behavior have bounded gradients, whereas the gradients of RNNs with chaotic dynamics always diverge. Based on these analyses and insights we suggest ways of how to optimize the training process on chaotic data according to the system's Lyapunov spectrum, regardless of the employed RNN architecture

Generalized Teacher Forcing for Learning Chaotic Dynamics

Chaotic dynamical systems (DS) are ubiquitous in nature and society. Often we are interested in reconstructing such systems from observed time series for prediction or mechanistic insight, where by reconstruction we mean learning geometrical and invariant temporal properties of the system in question (like attractors). However, training reconstruction algorithms like recurrent neural networks (RNNs) on such systems by gradient-descent based techniques faces severe challenges. This is mainly due to exploding gradients caused by the exponential divergence of trajectories in chaotic systems. Moreover, for (scientific) interpretability we wish to have as low dimensional reconstructions as possible, preferably in a model which is mathematically tractable. Here we report that a surprisingly simple modification of teacher forcing leads to provably strictly all-time bounded gradients in training on chaotic systems, and, when paired with a simple architectural rearrangement of a tractable RNN design, piecewise-linear RNNs (PLRNNs), allows for faithful reconstruction in spaces of at most the dimensionality of the observed system. We show on several DS that with these amendments we can reconstruct DS better than current SOTA algorithms, in much lower dimensions. Performance differences were particularly compelling on real world data with which most other methods severely struggled. This work thus led to a simple yet powerful DS reconstruction algorithm which is highly interpretable at the same time.Comment: Published in the Proceedings of the 40th International Conference on Machine Learning (ICML 2023

Blockchain Adoption for Trusted Supply Chain: A Preliminary Study of Key Determinants

Despite the promising capability to help organisations inaugurate their trusted supply chains, blockchain technology has not been widely adopted due to several adoption inhibitors. As opposed to prior blockchain literature that mostly focused on the direct effect of technology-related factors, this research-in-progress paper offers a new perspective of holistic examination of key determinants of an organisation’s intention to adopt blockchain. The paper also develops a conceptual model of blockchain adoption for supply chain context grounded on the integration of the technology-organisation-environment (TOE) and the extended valence frameworks. Specifically, three technological factors, two organisational factors, and two environmental factors directly influence an organisation’s intention to adopt blockchain. Moreover, trust influences blockchain adoption intention indirectly through performance expectancy and risk of system security. The study offers a contribution to academics regarding the critical determinants of blockchain deployment for establishing trusted supply chains

Bifurcations and loss jumps in RNN training

Recurrent neural networks (RNNs) are popular machine learning tools for modeling and forecasting sequential data and for inferring dynamical systems (DS) from observed time series. Concepts from DS theory (DST) have variously been used to further our understanding of both, how trained RNNs solve complex tasks, and the training process itself. Bifurcations are particularly important phenomena in DS, including RNNs, that refer to topological (qualitative) changes in a system's dynamical behavior as one or more of its parameters are varied. Knowing the bifurcation structure of an RNN will thus allow to deduce many of its computational and dynamical properties, like its sensitivity to parameter variations or its behavior during training. In particular, bifurcations may account for sudden loss jumps observed in RNN training that could severely impede the training process. Here we first mathematically prove for a particular class of ReLU-based RNNs that certain bifurcations are indeed associated with loss gradients tending toward infinity or zero. We then introduce a novel heuristic algorithm for detecting all fixed points and k-cycles in ReLU-based RNNs and their existence and stability regions, hence bifurcation manifolds in parameter space. In contrast to previous numerical algorithms for finding fixed points and common continuation methods, our algorithm provides exact results and returns fixed points and cycles up to high orders with surprisingly good scaling behavior. We exemplify the algorithm on the analysis of the training process of RNNs, and find that the recently introduced technique of generalized teacher forcing completely avoids certain types of bifurcations in training. Thus, besides facilitating the DST analysis of trained RNNs, our algorithm provides a powerful instrument for analyzing the training process itself